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Can the LattePanda Sigma (16 GB) run Granite 4.0 H Tiny (7B-A1B)?

42Fair quality (C) · 42 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 74/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs great

Runs great. At Q5_K_M it needs about 6.2 GB of 8.0 GB usable, with an estimated speed of 62.1 tok/s.

Best quant
Q5_K_M
Generation speed
62.1 tok/s
Prompt processing
2,035 tok/s
First token (1K prompt)
0.5 s
Largest safe context
128K

Best app to run it on the LattePanda Sigma (16 GB)

On Windows

llama.cpp

Command line + local API · runs on llama.cpp

  • Supports Intel Arc through SYCL and Vulkan.
  • Usually the first to support new model architectures.
llama-server -hf ibm-granite/granite-4.0-h-tiny-GGUF:Q5_K_M
  • LM Studio Point-and-click: search, download and chat in one window.
  • Ollama Intel GPU support here is experimental (Vulkan).

On Linux

llama.cpp

Command line + local API · runs on llama.cpp

  • Supports Intel Arc through SYCL and Vulkan.
  • Usually the first to support new model architectures.
llama-server -hf ibm-granite/granite-4.0-h-tiny-GGUF:Q5_K_M
  • LM Studio Point-and-click: search, download and chat in one window.
  • Ollama Intel GPU support here is experimental (Vulkan).

Want an agent? Granite 4.0 H Tiny (7B-A1B) supports tool use, so you can pair a llama.cpp or MLX LM server with one of these. Apps that run on this computer come first.

Coding agents

Personal assistants

How we choose: the files published for this model, your operating system and GPU, whether it fits fully in memory, and published speed tests (MLX vs llama.cpp on Apple silicon, vLLM vs llama.cpp vs Ollama on NVIDIA). Details.

Download Granite 4.0 H Tiny (7B-A1B)

Recommended for the LattePanda Sigma (16 GB): Q5_K_M. Every other quantization has its own download link in the table below.

Quickest · Ollama

One command downloads a ready-to-run version and starts a chat.

Get Ollama at ollama.com/download (opens in a new tab) · model page (opens in a new tab)

ollama run granite4:tiny-h

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

Download the Q5_K_M file (opens in a new tab)

huggingface.co/ibm-granite/granite-4.0-h-tiny-GGUF (opens in a new tab)

ollama run hf.co/ibm-granite/granite-4.0-h-tiny-GGUF:Q5_K_M

Original weights · Hugging Face

Full-precision files from IBM, for fine-tuning, vLLM, Transformers.

huggingface.co/ibm-granite/granite-4.0-h-tiny (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from IBM. The weights themselves download from Hugging Face.

github.com/ibm-granite/granite-4.0-language-models (opens in a new tab)

IBM’s official Granite 4.0 repo.

git clone https://github.com/ibm-granite/granite-4.0-language-models

Links checked 3 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 42Fair quality (C) · 42 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 74/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q5_K_M, from its editorial quality tier scaled by the quantization or precision. It’s the same on any hardware that runs this variant. Scale: A+ 85+ · A 70+ · B 55+ · C 40+ · D under 40.
Runs Runs great
How well it runs on the LattePanda Sigma (16 GB): speed, capped by how well it fits in memory. Runs great: 30+ tokens/s (or near the image, video or decision speed target) · Runs well: 10+ tokens/s · Slow: 3+ tokens/s · Very slow: under 3 · Won’t run: doesn’t fit even with offload. A tight fit or RAM offload caps it at Runs well; heavy offload caps it at Slow.

Match score

To order lists, each result also gets a match score out of 100 from four parts. It favors the most capable model that still runs well.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.19.2 / 45
Memory fitFull marks when the model fits in the share of unified memory the GPU can use; fewer when it goes past that limit.25.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.15.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.15.0 / 15
Match score74.2 / 100

The match score orders results; it is not shown as a grade. Quality and Runs are the two ratings in the tables.

Every quantization on the LattePanda Sigma (16 GB)

Quant Memory needed Available Speed Runs Quality Download
Q2_K 4.1 GB 8.0 GB 106 tok/s Runs great 30Basic quality (D) · 30 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 65/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q3_K_M 4.7 GB 8.0 GB 88.5 tok/s Runs great 36Basic quality (D) · 36 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 70/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q4_K_M 5.5 GB 8.0 GB 72.6 tok/s Runs great 41Fair quality (C) · 41 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 6.2 GB 8.0 GB 62.1 tok/s Runs great 42Fair quality (C) · 42 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 74/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 7.0 GB 8.0 GB 53.7 tok/s Runs welltight fit 43Fair quality (C) · 43 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q8_0 8.7 GB 8.0 GB 35.0 tok/s Runs wellover GPU memory limit 44Fair quality (C) · 44 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 58/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 15.2 GB 8.0 GB — Won't run 44Fair quality (C) · 44 of 100: how capable the model is at F16, whatever it runs on. Match score 5/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab) Original weights (opens in a new tab)

Memory figures assume 8K of context. Longer conversations or documents grow the KV cache.

Other hardware that runs it

Quick answers

How much memory does Granite 4.0 H Tiny (7B-A1B) need on the LattePanda Sigma 16GB?

At Q5_K_M it needs about 6.2 GB. The LattePanda Sigma 16GB has 8.0 GB usable.

How fast is Granite 4.0 H Tiny (7B-A1B) on the LattePanda Sigma 16GB?

About 62.1 tok/s at Q5_K_M, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Granite 4.0 H Tiny (7B-A1B) should I use on the LattePanda Sigma 16GB?

Q5_K_M. Of the versions that fit, it is the one that runs best; when two run equally well, the check prefers the higher-rated one.

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.

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